Indirect System Condition Monitoring Using Online Bayesian Changepoint Detection
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
This paper presents a method for online vibration analysis and a simple test bench analogue for the solder pumping system in an industrial wave-soldering machine at a Siemens factory. A common machine fault is caused by solder build-up within the pipes of the machine. This leads to a pressure drop in the system, which is replicated in the test bench by restricting the flow of water using a gate valve. The pump’s vibrational response is recorded using an accelerometer. The captured data is passed through an online Bayesian Changepoint Detection algorithm, adapted from existing literature, to detect the point at which the change in flow rate affects the pump, and thus the PCB assembly capability of the machine. This information can be used to trigger machine maintenance operations, or to isolate the vibrational response indicative of the machine fault.
Details
Original language | English |
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Title of host publication | Smart Technologies for Precision Assembly - 9th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2020, Revised Selected Papers |
Editors | Svetan Ratchev |
Pages | 81-92 |
Number of pages | 12 |
Publication status | Published - 2 Apr 2021 |
Peer-reviewed | Yes |
Publication series
Series | Smart Technologies for Precision Assembly |
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Volume | 620 |
ISSN | 1868-4238 |
External IDs
Scopus | 85107427436 |
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ORCID | /0000-0001-6734-704X/work/142235738 |
Keywords
ASJC Scopus subject areas
Keywords
- Bayesian changepoint detection, Industrial application, Predictive maintenance